Week 5: Ad Spend and Election Outcomes

Advertising and Election Outcomes

In this post, I will investigate the effects that ad spending can have on the outcome of elections on a district by district level. Using my current model, I will incorporate data found on the FEC’s website to approximate ad spend on a campaign district level. Using how much money was spent will be my variable, though I am aware that some scholarship says that number of ads rather than money spent is also a good indicator. I will also run tests on that next week. I will also be using data from the 2018 midterm to build my predictive model.

##Current Scholarship##

Political scientists Gregory A Huber and Kevin Arceneaux have noted in their research that most districts tend to be non-competitive. They also argue that “Advertising does a little to inform, next to nothing to mobilize, and a great deal to persuade potential voters.” A key takeaway from their work is how campaign advertising can do a great deal to persuade potential voters to candidates. Therefore, for undecided voters, it is highly likely that receiving more of a certain candidates ads more often than another will increase the likelihood of that voter to vote for the candidate which they saw more ads for.

This has pretty important implications and would be useful to include into our model and test whether or not the amount campaigns spend on ads and air space can affect our model’s prediction and accuracy for both 2018 and the upcoming 2022 election.

My Existing Model

New local model that includes district level data on polling, incumbency, and local employment data is much more accurate. R-squared of 0.73 which is the highest so far. This is only when I use for DemMajorVotePct. When I do DemSeats(which my previous models used) as my outcome variable, I get a lower R-squared of 0.50 exactly.

Below is a plot of the actual outcomes from the 2018 election from which im pulling my predictive modeling data from.

Now I have to add the variable of ad spending on a local level. I’ve gone ahead and downloaded data from the FEC for 2018 election spending data. This isn’t exactly the ad spend per campaign but I am using it as a proxy by making the assumption that the more money a particular race / candidate has overall translates to how much it is spending in ads.

Observations 448
Dependent variable DemVotesMajorPercent
Type OLS linear regression
F(4,443) 312.97
0.74
Adj. R² 0.74
Est. S.E. t val. p
(Intercept) 77.96 3.42 22.77 0.00
avg -6.73 0.19 -35.15 0.00
Unemployed_prct 0.35 0.89 0.39 0.70
winner_candidate_incIncumbent 4.96 1.04 4.79 0.00
Receipts -0.00 0.00 -3.02 0.00
Standard errors: OLS

References

Bafumi, J., Erikson, R., & Wlezien, C. (2018). Forecasting the 2018 Midterm Election using National Polls and District Information. PS: Political Science & Politics, 51(S1), 7-11. doi:10.1017/S1049096518001579

Ballotpedia. (2018). United States House of Representatives elections, 2018. https://ballotpedia.org/United_States_House_of_Representatives_elections,_2018

Ballotpedia. (2022). United States Congress elections, 2022. https://ballotpedia.org/United_States_Congress_elections,_2022

Congressional candidate data summary tables - FEC.gov. (2022). Retrieved 16 October 2022, from https://www.fec.gov/campaign-finance-data/congressional-candidate-data-summary-tables/?year=2022&segment=18

Cook Political Report. (2022). PVI Map and District List. https://www.cookpolitical.com/cook-pvi/2022-partisan-voting-index/district-map-and-list

Gerber, A.S., Gimpel, J. G., Green, D. P., & Shaw, D. R. (2011). How Large and Long-lasting Are the Persuasive Effects of Televised Campaign Ads? Results from a Randomized Field Experiment. American Political Science Review, 105(1), 135–150. https://doi.org/10.1017/S000305541000047X

Wesleyan Media Project. (2022, October 6). Democrats Out-Pacing GOP in Senate Races. https://mediaproject.wesleyan.edu/releases-100622/

Week 4: Incumbancy and Election Outcomes

Expert Ratings In my previous blogs, I have focused solely at national variables and conditions which may affect our election outcomes. Things like GDP, unemployment, polling, etc. are very helpful tools but we also have the option to look at elections from a much closer, district level approach. On the district level, many experts watch and weigh in on important races. Organizations such as Cook Political Report and Sabato’s Crystal Ball take into account local conditions and determine ratings on an individual district level basis. [Read More]

Week 3: Polling and Elections

This weeks blog post will focus on incorporating polling data to add into my existing predictive model that already includes economy variables to test to see how our predictions improve.

Introduction This week, we focused on seeing how polling might affect and predict election outcomes. Gelman and King spoke about how polling is really only indicative of people’s preferences close to the election. Over the course of a campaign cycle, Gelman and King argue, responses to pollsters during the campaign are not generally informed or even, in a sense we describe, “rational.” For this reason, when building and assessing a model this week, I will try to weigh the polls heavier when they are closer to the election day. [Read More]

Week 2: Elections and the Economy

This weeks blog post will focus on using the economy as a variable to predictive modeling for the upcoming election using past data such as the unemployment rate, GDP for each quarter, RDI, etc.

This weeks blog post will focus on using the economy as a variable to predictive modeling for the upcoming election using past data such as the unemployment rate, GDP for each quarter, RDI, etc. Introduction There has been extensive research showing that prospective voters substantially consider the state of the economy when making electoral decisions. Christopher H. Achen’s and Larry M. Bartels in Democracy For Realists argue that voters use the economy as a way to measure the performance of an incumbent party / president. [Read More]

Week 1: Elections

This is my first weekly election blog posting for an ongoing assignment for the class Election Analytics at Harvard College with Professor Ryan Enos. Enjoy! :) I will be using the visualization customization extension for this blog post and will use the resulting data to make a prediction about the upcoming 2022 midterm election. create a map of Republican/Democrat voteshare margin by state in a year of your choice, create a map of Republican/Democrat voteshare margin by state and congressional district in 2014, label each state (e. [Read More]